Move to PY3.

PiperOrigin-RevId: 313217111

scratchgan/experiment.py

@@ -1,3 +1,4 @@
+# Lint as: python3
 # Copyright 2019 DeepMind Technologies Limited and Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -109,7 +110,7 @@ def train(config):
   raw_data = reader.get_raw_data(
       data_path=config.data_dir, dataset=config.dataset)
   train_data, valid_data, word_to_id = raw_data
-  id_to_word = {v: k for k, v in word_to_id.iteritems()}
+  id_to_word = {v: k for k, v in word_to_id.items()}
   vocab_size = len(word_to_id)
   max_length = reader.MAX_TOKENS_SEQUENCE[config.dataset]
   logging.info("Vocabulary size: %d", vocab_size)
@@ -124,8 +125,8 @@ def train(config):
       name="real_sequence")
   real_sequence_length = tf.placeholder(
       dtype=tf.int32, shape=[config.batch_size], name="real_sequence_length")
-  first_batch_np = iterator.next()
+  first_batch_np = next(iterator)
-  valid_batch_np = iterator_valid.next()
+  valid_batch_np = next(iterator_valid)
 
   test_real_batch = {k: tf.constant(v) for k, v in first_batch_np.items()}
   test_fake_batch = {
@@ -146,7 +147,7 @@ def train(config):
     embedding_source = utils.get_embedding_path(config.data_dir, config.dataset)
     vocab_file = "/tmp/vocab.txt"
     with gfile.GFile(vocab_file, "w") as f:
-      for i in xrange(len(id_to_word)):
+      for i in range(len(id_to_word)):
         f.write(id_to_word[i] + "\n")
     logging.info("Temporary vocab file: %s", vocab_file)
   else:
@@ -263,17 +264,17 @@ def train(config):
     if latest_ckpt:
       saver.restore(sess, latest_ckpt)
 
-    for step in xrange(config.num_steps):
+    for step in range(config.num_steps):
-      real_data_np = iterator.next()
+      real_data_np = next(iterator)
       train_feed = {
           real_sequence: real_data_np["sequence"],
           real_sequence_length: real_data_np["sequence_length"],
       }
 
       # Update generator and discriminator.
-      for _ in xrange(config.num_disc_updates):
+      for _ in range(config.num_disc_updates):
         sess.run(disc_update, feed_dict=train_feed)
-      for _ in xrange(config.num_gen_updates):
+      for _ in range(config.num_gen_updates):
         sess.run(gen_update, feed_dict=train_feed)
 
       # Reporting
@@ -325,7 +326,7 @@ def evaluate_pair(config, batch_size, checkpoint_path, data_dir, dataset,
   # Build graph.
   train_data, valid_data, word_to_id = reader.get_raw_data(
       data_dir, dataset=dataset)
-  id_to_word = {v: k for k, v in word_to_id.iteritems()}
+  id_to_word = {v: k for k, v in word_to_id.items()}
   vocab_size = len(word_to_id)
   train_iterator = reader.iterator(raw_data=train_data, batch_size=batch_size)
   valid_iterator = reader.iterator(raw_data=valid_data, batch_size=batch_size)
@@ -353,7 +354,7 @@ def evaluate_pair(config, batch_size, checkpoint_path, data_dir, dataset,
     embedding_source = utils.get_embedding_path(config.data_dir, config.dataset)
     vocab_file = "/tmp/vocab.txt"
     with gfile.GFile(vocab_file, "w") as f:
-      for i in xrange(len(id_to_word)):
+      for i in range(len(id_to_word)):
         f.write(id_to_word[i] + "\n")
     logging.info("Temporary vocab file: %s", vocab_file)
   else:
@@ -431,10 +432,10 @@ def evaluate_pair(config, batch_size, checkpoint_path, data_dir, dataset,
     logging.info("Restoring variables.")
     saver.restore(sess, checkpoint_path)
 
-    for i in xrange(num_batches):
+    for i in range(num_batches):
       logging.info("Batch %d / %d", i, num_batches)
-      train_data_np = train_iterator.next()
+      train_data_np = next(train_iterator)
-      valid_data_np = valid_iterator.next()
+      valid_data_np = next(valid_iterator)
       feed_dict = {
           train_sequence: train_data_np["sequence"],
           train_sequence_length: train_data_np["sequence_length"],

scratchgan/generators.py

@@ -1,3 +1,4 @@
+# Lint as: python3
 # Copyright 2019 DeepMind Technologies Limited and Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -104,7 +105,7 @@ class LSTMGen(snt.AbstractModule):
     sample = tf.tile(
         tf.constant(self._pad_token, dtype=tf.int32)[None], [batch_size])
     logging.info('Unrolling over %d steps.', max_sequence_length)
-    for _ in xrange(max_sequence_length):
+    for _ in range(max_sequence_length):
       # Input is sampled word at t-1.
       embedding = tf.nn.embedding_lookup(input_embeddings, sample)
       embedding.shape.assert_is_compatible_with(

scratchgan/losses.py

@@ -1,3 +1,4 @@
+# Lint as: python3
 # Copyright 2019 DeepMind Technologies Limited and Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -67,9 +68,9 @@ def reinforce_loss(disc_logits, gen_logprobs, gamma, decay):
   # Compute cumulative rewards.
   rewards_list = tf.unstack(rewards, axis=1)
   cumulative_rewards = []
-  for t in xrange(sequence_length):
+  for t in range(sequence_length):
     cum_value = tf.zeros(shape=[batch_size])
-    for s in xrange(t, sequence_length):
+    for s in range(t, sequence_length):
       cum_value += np.power(gamma, (s - t)) * rewards_list[s]
     cumulative_rewards.append(cum_value)
   cumulative_rewards = tf.stack(cumulative_rewards, axis=1)

scratchgan/reader.py

@@ -1,3 +1,4 @@
+# Lint as: python3
 # Copyright 2019 DeepMind Technologies Limited and Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -56,17 +57,17 @@ def _build_vocab(json_data):
       title_tokens = tokenize(title)
       vocab.update(title_tokens)
   # Most common words first.
-  count_pairs = sorted(vocab.items(), key=lambda x: (-x[1], x[0]))
+  count_pairs = sorted(list(vocab.items()), key=lambda x: (-x[1], x[0]))
-  words, _ = zip(*count_pairs)
+  words, _ = list(zip(*count_pairs))
   words = list(words)
   if UNK not in words:
     words = [UNK] + words
-  word_to_id = dict(zip(words, range(len(words))))
+  word_to_id = dict(list(zip(words, list(range(len(words))))))
 
   # Tokens are now sorted by frequency. There's no guarantee that `PAD` will
   # end up at `PAD_INT` index. Enforce it by swapping whatever token is
   # currently at the `PAD_INT` index with the `PAD` token.
-  word = word_to_id.keys()[word_to_id.values().index(PAD_INT)]
+  word = list(word_to_id.keys())[list(word_to_id.values()).index(PAD_INT)]
   word_to_id[PAD], word_to_id[word] = word_to_id[word], word_to_id[PAD]
   assert word_to_id[PAD] == PAD_INT
 
@@ -120,7 +121,7 @@ def get_raw_data(data_path, dataset, truncate_vocab=20000):
   """
   if dataset not in FILENAMES:
     raise ValueError("Invalid dataset {}. Valid datasets: {}".format(
-        dataset, FILENAMES.keys()))
+        dataset, list(FILENAMES.keys())))
   train_file, valid_file, _ = FILENAMES[dataset]
 
   train_path = os.path.join(data_path, train_file)

scratchgan/utils.py

@@ -1,3 +1,4 @@
+# Lint as: python3
 # Copyright 2019 DeepMind Technologies Limited and Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -34,7 +35,7 @@ def _get_embedding_initializer(vocab_file, embedding_source, vocab_size):
     embedding_lines = f.readlines()
 
   # First line contains embedding dim.
-  _, embedding_dim = map(int, embedding_lines[0].split())
+  _, embedding_dim = list(map(int, embedding_lines[0].split()))
   # Get the tokens as strings.
   tokens = [line.split()[0] for line in embedding_lines[1:]]
   # Get the actual embedding matrix.
@@ -275,6 +276,12 @@ def make_partially_trainable_embeddings(vocab_file, embedding_source,
   vectors for word representation. In Proceedings of the 2014 conference on
   empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
 
+  Args:
+    vocab_file: vocabulary file.
+    embedding_source: path to the actual embeddings.
+    vocab_size: number of words in vocabulary.
+    trainable_embedding_size: size of the trainable part of the embeddings.
+
   Returns:
     A matrix of partially pretrained embeddings.
   """